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study_definition.py
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study_definition.py
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# Adapted from William Hume's Tutorial 3: https://nbviewer.jupyter.org/github/opensafely/os-demo-research/blob/master/rmarkdown/Rdemo.html
# Import functions
from cohortextractor import (
StudyDefinition,
patients,
codelist,
codelist_from_csv,
combine_codelists,
Measure
)
# Import codelists
from codelists import *
# Specifiy study definition
start_date = "2020-12-07"
end_date = "2020-12-31"
study = StudyDefinition(
index_date = "2020-12-07",
# Configure the expectations framework
default_expectations={
"date": {"earliest": start_date, "latest": end_date},
"rate": "exponential_increase",
"incidence":1
},
# Study population
population = patients.satisfying(
"""
(age_ !=0) AND
(NOT died) AND
(registered)
""",
died = patients.died_from_any_cause(
on_or_before="index_date",
returning="binary_flag"
),
registered = patients.registered_as_of("index_date"),
age_=patients.age_as_of("index_date"),
),
#### Sociodemographics : age ; sex ; imd ; ethnicity
age=patients.age_as_of(
"index_date",
return_expectations={
"rate": "universal",
"int": {"distribution": "population_ages"},
},
),
age_band=patients.categorised_as(
{
"0": "DEFAULT",
"0-19": """ age >= 0 AND age < 20""",
"20-29": """ age >= 20 AND age < 30""",
"30-39": """ age >= 30 AND age < 40""",
"40-49": """ age >= 40 AND age < 50""",
"50-59": """ age >= 50 AND age < 60""",
"60-69": """ age >= 60 AND age < 70""",
"70-79": """ age >= 70 AND age < 80""",
"80+": """ age >= 80 AND age < 120""",
},
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"0": 0.001,
"0-19": 0.124,
"20-29": 0.125,
"30-39": 0.125,
"40-49": 0.125,
"50-59": 0.125,
"60-69": 0.125,
"70-79": 0.125,
"80+": 0.125,
}
},
},
),
sex=patients.sex(
return_expectations={
"rate": "universal",
"category": {"ratios": {"M": 0.49, "F": 0.5, "U": 0.01}},
}
),
# Ethnicity (6 categories)
ethnicity=patients.with_these_clinical_events(
ethnicity_codes,
returning="category",
find_last_match_in_period=True,
return_expectations={
"category": {"ratios": {"1": 0.2, "2":0.2, "3":0.2, "4":0.2, "5": 0.2}},
"incidence": 0.75,
},
),
# IMD
# https://github.com/opensafely/risk-factors-research/issues/45
# https://github.com/opensafely/covid-vaccine-effectiveness-research/blob/5c2aedebe1fe4b238765d1e12d9086cb34f8924c/analysis/study_definition.py
imd=patients.categorised_as(
{
"U": "DEFAULT",
"Q1": """index_of_multiple_deprivation >=1 AND index_of_multiple_deprivation < 32844*1/5""",
"Q2": """index_of_multiple_deprivation >= 32844*1/5 AND index_of_multiple_deprivation < 32844*2/5""",
"Q3": """index_of_multiple_deprivation >= 32844*2/5 AND index_of_multiple_deprivation < 32844*3/5""",
"Q4": """index_of_multiple_deprivation >= 32844*3/5 AND index_of_multiple_deprivation < 32844*4/5""",
"Q5": """index_of_multiple_deprivation >= 32844*4/5 AND index_of_multiple_deprivation < 32844""",
},
index_of_multiple_deprivation=patients.address_as_of(
"index_date",
returning="index_of_multiple_deprivation",
round_to_nearest=100,
),
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"U": 0.05,
"Q1": 0.19,
"Q2": 0.19,
"Q3": 0.19,
"Q4": 0.19,
"Q5": 0.19,
}
},
},
),
#### Location / Registration
#
#
# NUTS1 Region
region=patients.registered_practice_as_of(
"index_date",
returning="nuts1_region_name",
return_expectations={
"rate": "universal",
"category": {
"ratios": {
"North East": 0.1,
"North West": 0.1,
"Yorkshire and the Humber": 0.1,
"East Midlands": 0.1,
"West Midlands": 0.1,
"East of England": 0.1,
"London": 0.2,
"South East": 0.2,
},
},
},
),
# STP
# https://github.com/opensafely/risk-factors-research/issues/44
stp=patients.registered_practice_as_of(
"index_date",
returning="stp_code",
return_expectations={
"rate": "universal",
"category": {"ratios": {"STP1": 0.5, "STP2": 0.5}},
},
),
# Practice
practice = patients.registered_practice_as_of(
"index_date",
returning = "pseudo_id",
return_expectations={
"int": {"distribution": "normal", "mean": 100, "stddev": 20}
},
),
#### CONSULTATION INFORMATION
#
#
gp_consult_count=patients.with_gp_consultations(
between = ["index_date", "index_date + 1 month"],
returning="number_of_matches_in_period",
return_expectations={
"int": {"distribution": "normal", "mean": 4, "stddev": 2},
"incidence": 0.7,
},
),
# count of occurrences / matches
OC_SNOMED=patients.with_these_clinical_events(
oc_local_codes_snomed,
between = ["index_date", "index_date + 1 month"],
returning="number_of_matches_in_period",
return_expectations={
"incidence": 0.5,
"int": {"distribution": "normal", "mean": 3, "stddev": 0.5}},
),
# boolean for occurrence in period
event_x =patients.with_these_clinical_events(
codelist=oc_local_codes_snomed,
between=["index_date", "index_date + 1 month"],
returning="binary_flag",
return_expectations={"incidence": 0.5}
),
# returns /first/ type of code matched in period?
event_x_event_code=patients.with_these_clinical_events(
codelist=oc_local_codes_snomed,
between=["index_date", "index_date + 1 month"],
returning="code",
return_expectations={"category": {
"ratios": {
"1090371000000106":0.1,
"1068881000000101": 0.1,
"978871000000104": 0.05,
"854891000000104": 0.05,
"384131000000101": 0.05,
"325991000000105": 0.05,
"325981000000108": 0.05,
"325951000000102": 0.05,
"325911000000101": 0.05,
"325901000000103":0.05,
"325871000000103":0.05,
"868184008":0.05,
"763184009":0.05,
"719407002":0.05,
"699249000":0.05,
"448337001":0.05,
"401271004":0.05,
"185320006":0.05
},
}, }
),
)
measures = [
Measure(
id="1_total",
numerator="event_x",
denominator="population",
group_by=None
),
Measure(
id="1_event_code",
numerator="event_x",
denominator="population",
group_by=["event_x_event_code"]
),
Measure(
id="1_practice_only",
numerator="event_x",
denominator="population",
group_by=["practice"]
),
Measure(
id="1_by_region",
numerator="event_x",
denominator="population",
group_by=["region"]
),
Measure(
id="1_by_sex",
numerator="event_x",
denominator="population",
group_by=["sex"]
),
Measure(
id="1_by_age_band",
numerator="event_x",
denominator="population",
group_by=["age_band"]
),
Measure(
id="1_by_imd",
numerator="event_x",
denominator="population",
group_by=["imd"]
),
Measure(
id="1_by_ethnicity",
numerator="event_x",
denominator="population",
group_by=["ethnicity"]
),
]